An algorithm for finding nearest neighbours in (approximately) constant average time
Pattern Recognition Letters
Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
Graph drawing by force-directed placement
Software—Practice & Experience
Fundamentals of speech recognition
Fundamentals of speech recognition
A linear iteration time layout algorithm for visualising high-dimensional data
Proceedings of the 7th conference on Visualization '96
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Some approaches to best-match file searching
Communications of the ACM
Does organisation by similarity assist image browsing?
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
ACM Computing Surveys (CSUR)
Fixed Queries Array: A Fast and Economical Data Structure for Proximity Searching
Multimedia Tools and Applications
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A Fast Adaptive Layout Algorithm for Undirected Graphs
GD '94 Proceedings of the DIMACS International Workshop on Graph Drawing
On the Efficiency of Nearest Neighbor Searching with Data Clustered in Lower Dimensions
ICCS '01 Proceedings of the International Conference on Computational Sciences-Part I
Case Study: Visualizing Sets of Evolutionary Trees
INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02)
A Hybrid Layout Algorithm for Sub-Quadratic Multidimensional Scaling
INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02)
ACE: A Fast Multiscale Eigenvectors Computation for Drawing Huge Graphs
INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02)
The InfoSky visual explorer: exploiting hierarchical structure and document similarities
Information Visualization
Fast multidimensional scaling through sampling, springs and interpolation
Information Visualization
Information Visualization - Special issue on coordinated and multiple views in exploratory visualization
Audio-based event detection for sports video
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Improving hybrid MDS with pivot-based searching
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
A visual workspace for hybrid multidimensional scaling algorithms
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
Incremental board: a grid-based space for visualizing dynamic data sets
Proceedings of the 2009 ACM symposium on Applied Computing
An incremental space to visualize dynamic data sets
Multimedia Tools and Applications
A visual framework to understand similarity queries and explore data in Metric Access Methods
International Journal of Business Intelligence and Data Mining
Visualizing gene co-expression as google maps
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Human-centered visualization environments
Human-centered visualization environments
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The problem of exploring or visualising data of high dimensionality is central to many tools for information visualisation. Through representing a data set in terms of inter-object proximities, multidimensional scaling may be employed to generate a configuration of objects in low-dimensional space in such a way as to preserve high-dimensional relationships. An algorithm is presented here for a heuristic hybrid model for the generation of such configurations. Building on a model introduced in 2002, the algorithm functions by means of sampling, spring model and interpolation phases. The most computationally complex stage of the original algorithm involved the execution of a series of nearest-neighbour searches. In this paper, we describe how the complexity of this phase has been reduced by treating all high-dimensional relationships as a set of discretised distances to a constant number of randomly selected items: pivots. In improving this computational bottleneck, the algorithmic complexity is reduced from O(N√N) to O(N5/4). As well as documenting this improvement, the paper describes evaluation with a data set of 108,000 13-dimensional items and a set of 23,141 17-dimensional items. Results illustrate that the reduction in complexity is reflected in significantly improved run times and that no negative impact is made upon the quality of layout produced.